11818results about How to "Extend your life" patented technology

The invention relates to power distribution network fault processing equipment applied to the technical field of power distribution, and aims to improve the fault prediction, diagnosis and processing capabilities of a power system. The equipment comprises a pre-training module, a neural network architecture module, a data acquisition layer, an edge calculation layer, a core processing layer, a context learning module, a reasoning processing module, an output and interaction layer and a continuous learning and improvement layer, and is characterized in that a large-scale pre-trained Transform model is utilized, and professional knowledge of a power system is combined; according to the method, deep understanding and analysis of a complex power grid state are realized, through multi-source data fusion, edge calculation and central cooperative processing, the equipment can monitor the power grid state in real time, accurately predict potential faults and provide intelligent diagnosis and repair suggestions, and model pre-training and optimization are performed by adopting self-supervised learning and federated learning technologies; using reinforcement learning to optimize a fault response strategy; the interpretability of the model is realized, and the decision transparency is improved.

The invention provides a die-casting die heat balance distribution optimization method and system based on an energy model, and relates to the technical field of die-casting dies, and the method comprises the steps: collecting heat distribution data, and building a heat energy transfer mapping relation; extracting space-time correlation features by using a graph attention network; constructing a heat distribution dynamic evolution model; constructing a temperature field predictor based on a deep belief network; training a predictor by using a multi-agent coevolution algorithm; constructing a temperature field optimization strategy network; generating an optimal temperature field control sequence; establishing a regional adaptive control strategy; constructing a temperature field controller based on deep reinforcement learning; and a dynamic fuzzy immune optimization algorithm is adopted to adjust cooling control parameters, and intelligent optimization control over heat distribution of the die-casting die is achieved.

The invention discloses an intelligent adjusting system of an adaptive multi-environment ship electric control valve actuator, which relates to the technical field of control valve adjustment and comprises an environment judgment module, a real-time monitoring module, an adaptive adjusting module and a feedback optimization module. The environment judgment module collects environment data through various sensors distributed on an electric control valve and around the electric control valve, and the environment data are processed through a signal conditioning and microprocessor application algorithm. According to the intelligent adjusting system of the self-adaptive multi-environment ship electric control valve actuator, the adaptability and the stability of the ship electric control valve actuator in variable environments are remarkably improved; the electric control valve actuator can monitor and respond to environment changes such as temperature, humidity, vibration and electromagnetic interference in real time, it is ensured that the electric control valve actuator can be accurately regulated and controlled under the extreme working condition, and therefore the reliability and safety of a ship system are improved.

The invention provides a pure electric vehicle charging control method and system, and relates to the technical field of electric vehicle energy storage, and the method comprises the steps: collecting real-time charging data, combining historical data to construct a battery state evaluation model, and calculating battery state information in real time; a multi-objective optimization model is established based on the battery state information, and an optimal charging strategy is solved by adopting an improved particle swarm algorithm; and a multi-dimensional safety monitoring system is established, a corresponding safety plan is executed when abnormal charging is detected, and the plan is fed back to the multi-target optimization model for strategy correction.

The invention relates to a thermal management control method and system for an energy storage power supply, and the method comprises the steps: obtaining the real-time temperature data of the energy storage power supply, and carrying out the heat source distribution analysis, and obtaining the corresponding temperature distribution information; heat dissipation operation parameters of the energy storage power supply are obtained, adjustment analysis is carried out in combination with the temperature distribution information, and a corresponding initial heat dissipation control strategy is obtained; acquiring real-time monitoring data of the energy storage power supply, and performing charge and discharge dynamic analysis in combination with the temperature distribution information and the initial heat dissipation control strategy to obtain corresponding charge and discharge adjustment information; based on the real-time temperature data and the thermal management strategy, hot spot identification is carried out, corresponding potential hot spot information is obtained, early warning processing is carried out, and a corresponding hot spot early warning result is obtained; and performing comprehensive control analysis on the thermal management strategy, the initial heat dissipation control strategy and the hot spot early warning result to obtain a corresponding global thermal management control scheme. According to the invention, comprehensive and intelligent control of thermal management of the energy storage power supply can be realized.

An energy scheduling system of a high-power liquid cooling charging pile belongs to the technical field of liquid cooling charging piles, and comprises a power scheduling module, a liquid cooling management module, a data acquisition and monitoring module, an energy storage and energy recovery module, a communication and interaction module, an intelligent scheduling module and a remote management and analysis module. Through the intelligent scheduling and energy optimization technology, power distribution can be adjusted in real time according to the vehicle demand and the power grid state, overload is avoided, and the charging efficiency is improved; and secondly, an advanced liquid cooling management technology is adopted, and temperature monitoring and cooling liquid flow rate adjustment are combined, so that heat balance and stable operation of the equipment under a high-load condition are ensured, the service life of the equipment is prolonged, and energy waste is reduced. In addition, the system can store redundant electric energy and release the electric energy in the peak load period of the power grid through the energy storage and energy recovery module, so that the power grid pressure is effectively reduced, and the energy utilization rate is also improved.

The invention provides a concrete production management method and system based on digital twinning, and relates to the technical field of digital twinning, and the method comprises the steps: collecting the health monitoring data of concrete production equipment in real time through a plurality of sensors, and collecting the full life cycle event data of the concrete production equipment; preprocessing the health monitoring data at an edge end, extracting state features of the equipment, performing real-time diagnosis by using a pre-trained fault prediction model based on the state features of the equipment, and constructing an equipment multi-level digital twinborn model at a cloud platform; the method comprises the following steps: fusing a pre-constructed physical degradation model and a data driving model based on a multi-level digital twinborn model, constructing an equipment residual life prediction model, and dynamically adjusting a concrete production strategy in the digital twinborn model according to an obtained prediction result to obtain an optimal concrete production scheduling scheme. And the information is converted into an equipment control instruction, so that automatic issuing and execution of a concrete production scheduling scheme are realized.

The invention discloses a battery life evaluation system and method based on different decline modes of a battery, and belongs to the technical field of battery management evaluation. Through combination of the data acquisition module and the information management module, battery manufacturing, operation and historical information is comprehensively acquired, accurate management of the whole life cycle of the battery is realized, the data analysis module and the performance evaluation module deeply analyze battery operation data, classify and identify recession modes and key influence factors, and the reliability of the battery is improved. The current performance and the remaining life of the battery are dynamically evaluated by combining a prediction model, decision making is simplified through health scoring, the battery management efficiency is improved, the adaptive performance optimization module adjusts battery parameters and temperature control strategies according to operation scenes, the performance and the life are balanced, active optimization and safety improvement are achieved, and the safety is improved. The carbon footprint evaluation module covers all stages of battery manufacturing, using and recycling, checks full-life-cycle carbon emission, traces the source of high emission links, provides low-carbon optimization suggestions, and assists enterprises in achieving the goal of green development.

The invention provides a remote control method and system for a new energy vehicle charging pile, and the method comprises the steps: collecting a vehicle battery state, a user demand, and the operation information of a power grid and the charging pile, and integrating the information into a standardized comprehensive state variable; dynamically adjusting the initial charging power of the single user according to the health state of the vehicle battery to obtain adjusted charging power; calculating the charging priority of the user by combining the distributed power with a dynamic weight adjustment model based on time decay; receiving a multi-user distribution strategy, and constructing a real-time adjustment variable set in combination with the operation information of the charging pile; and the power grid load change is monitored in real time. According to the invention, on one hand, a flexible solution is provided for personalized demands of users, and on the other hand, efficient utilization and active management of system resources are realized through energy sharing and remote dynamic control, so that main defects in the prior art are overcome, and the system has important technical value and application prospect.

The invention relates to the technical field of lithium battery charging and discharging management, and discloses a lithium battery charging and discharging detection and management method, device and equipment and a storage medium, and the method comprises the steps: carrying out the data collection of the charging and discharging process of a lithium battery pack, applying consistent Gaussian noise, and obtaining an electrode-level expansion data set and a battery-level expansion data set; performing feature mapping and feature mixing by minimizing the maximum average difference loss function to obtain a mapping feature vector; estimating the state of charge and the state of health of the battery through a fusion model of a recurrent neural network and a physical information hybrid neural network to obtain a state of charge estimated value and a state of health estimated value; and constructing a battery pack electric heating model of the lithium battery pack, and generating a corresponding charging and discharging control instruction sequence. Self-adaptive adjustment can be carried out according to the real-time state of the battery, potential safety hazards such as over-charging and over-discharging and out-of-control temperature are effectively avoided, and the use safety of the battery pack is remarkably improved.

The invention belongs to the technical field of condensation prevention of power equipment, and discloses an intelligent induction type condensation prevention system and method for the power equipment, and the system comprises a multivariate data collection unit, an intelligent control unit and an execution unit, and achieves the comprehensive real-time monitoring of the internal and external environments of the equipment through the integration of the multivariate data collection unit; the intelligent control unit can accurately predict the condensation risk based on real-time and historical data by using a pre-trained machine learning model, and dynamically adjust the condensation prevention strategy according to the condensation risk, thereby ensuring that condensation can be effectively prevented under various complex environmental conditions; the accurate execution of the execution unit further guarantees the condensation prevention effect, the operation stability and safety of the power equipment are integrally improved, the equipment fault risk caused by condensation is reduced, the service life of the equipment is prolonged, and the unnecessary maintenance cost is reduced; the system has self-learning and self-adaptive capabilities, can continuously optimize a control strategy to adapt to different environmental conditions, and ensures an optimal anti-condensation effect.

The invention relates to the technical field of mechanical joints, in particular to a humanoid robot linear electric drive joint which comprises a shell, a front end cover and a rear end cover, the front end cover and the rear end cover are fixed to the two ends of the shell respectively, and an execution assembly and a drive control assembly are arranged in the shell; the execution assembly comprises a reverse planetary roller screw and a motor, and the drive control assembly comprises an encoder composed of a coded disc and a magnetic ring, a pull pressure sensor and a driver. Aiming at the technical problem that an existing linear joint has defects, a reverse planetary roller lead screw is adopted as a main transmission component, and the lead screw, a roller and a nut of the reverse planetary roller lead screw are in linear contact, so that high precision, long service life, high integration level and high thrust density can be achieved while high bearing and high speed are achieved; therefore, the method is suitable for use scenes of humanoid robot joints.

The invention discloses a motor operation cycle monitoring and warning method and system, and relates to the technical field of motor monitoring, and the method comprises the steps: collecting motor operation data in real time through employing a distributed sensor network, and carrying out the analysis and processing of the collected original data through employing an edge computing device; transmitting the analyzed and processed data to a cloud data center, carrying out deep learning model training and feature extraction, and dividing a motor operation cycle based on the extracted features; and performing multi-dimensional anomaly detection on each divided operation cycle to generate an anomaly index, and performing motor operation cycle monitoring alarm according to the obtained anomaly index. The real-time performance and the reliability of the system can be improved. Compared with a traditional rule-based method, the method is more flexible and accurate, state changes of the motor can be evaluated more comprehensively, an early warning signal is sent out in time, and unnecessary downtime and maintenance cost are reduced.

The invention provides a high-altitude energy storage monitoring system. The system comprises an environmental data acquisition module, an intelligent temperature control analysis module, an intelligent energy scheduling module, a health monitoring module, a self-adaptive adjustment module and a system optimization module. The invention provides an intelligent and environment-adaptive energy storage system monitoring and maintenance strategy, the temperature control strategy, the energy distribution and the maintenance plan can be dynamically adjusted according to the real-time change of the high-altitude environment, the efficient operation of the energy storage system is realized, and the service life of equipment is prolonged. A sensor technology is used for collecting multi-dimensional environment data, and an intelligent algorithm is combined to monitor the operation state of the equipment in real time, predict the health state of the equipment, optimize energy scheduling and formulate a self-adaptive maintenance strategy, so that the fault risk and the maintenance cost are reduced, and the stable operation of the system in a complex environment is ensured.

The invention discloses a server and a heat dissipation control method and system thereof, a product, equipment and a medium, relates to the field of servers, and aims to solve the problem caused by too large or too small control rotating speed value. The method comprises the steps that a preset fan rotating speed range, a main heat dissipation partition and an auxiliary heat dissipation partition corresponding to each component in the server are determined; for each component, determining a current weight coefficient of each fan corresponding to the component, and obtaining a target rotating speed value of each fan corresponding to the component by using the actual temperature value of the component and the current weight coefficient of each fan corresponding to the component; and determining a control rotating speed value in the at least one target rotating speed value corresponding to each fan, and controlling the fan to operate according to the control rotating speed value. The control rotating speed value of each fan can be limited within the preset fan rotating speed range, the problem that the control rotating speed value is too large or too small is avoided, noise and energy consumption are reduced, the service life of the fan is prolonged, and the performance and stability of the server are kept.

The invention belongs to the field of fault diagnosis, and particularly relates to a main shaft bending common characteristic diagnosis system based on a stacking integration method, and the system comprises the following modules: a sensor module which is used for collecting a vibration signal of a main shaft in real time; the data acquisition and processing module is used for converting an analog signal into a digital signal and preprocessing the signal acquired by the sensor; the feature extraction module is used for extracting features from the preprocessed vibration signals; the machine learning diagnosis module analyzes and classifies the extracted features by using stack integration based on a plurality of trained machine learning models, and judges the health state of the main shaft; and the output module is used for outputting the diagnosis result and sending out an alarm signal when abnormality is detected. Real-time monitoring and automatic diagnosis of the main shaft bending fault are achieved, compared with a traditional method depending on expert experience and manual detection, the fault detection efficiency and accuracy can be greatly improved, and misjudgment and missed judgment risks caused by human factors are reduced.

The invention discloses a modular power supply efficiency intelligent management method, and aims to record the working state, power output and conversion efficiency of a power supply module in real time through a sensor and monitoring software, carry out system energy efficiency analysis regularly, and can significantly improve the overall performance of a power supply system. Firstly, according to the scheme, by optimizing power distribution and fault early warning, the energy efficiency can be effectively improved, energy waste is reduced, and the operation cost is reduced. Meanwhile, the system can dynamically adjust the load of each module, prolong the service life of equipment, reduce the equipment fault risk through preventive maintenance, and improve the stability and reliability of the system. In addition, an energy efficiency analysis report of the system provides a data-driven decision basis for a manager, helps to discover the potential of efficiency improvement, and guides the future optimization direction. And by reducing energy consumption, the power supply system can also reduce carbon emission, promote sustainable development of enterprises and improve environmental protection benefits.

The invention relates to the technical field of ship rust removal, in particular to a large ship surface self-adaptive rust removal method based on laser rust removal, and provides the following scheme that a ship structure design drawing is obtained, three-dimensional position marking is conducted on a rusted part, and depth data is measured; setting laser initial parameters according to the environmental data; constructing a parameter updating network, inputting the depth data and the initial parameters for training, and generating laser adjustment parameters; rust removal is conducted on the rusted part according to the adjusting parameters and the path planning; and finally, acquiring the surface roughness of the cleaned part and optimizing network parameters. According to the method, the laser power, frequency and scanning speed are adjusted in a self-adaptive mode, and the accuracy and efficiency of laser rust removal are improved.

The invention relates to the technical field of production process control, in particular to a PVC developing functional granule production control method and system, and the method comprises the steps: calling a spectrometer to capture spectral data emitted from PVC granules in real time based on the starting of a production line, analyzing the intensity and wavelength distribution in the spectral data in real time, and generating spectral data characteristic values; and comparing and analyzing the deviation of historical data and real-time data through the spectral data characteristic value, and generating a deviation dynamic prediction analysis result. According to the invention, by monitoring and analyzing the spectral data of the PVC granules in real time, the response speed and precision of a production line are improved, and the spectral data are compared in real time, so that production deviations such as light source intensity and reaction temperature can be quickly found and adjusted, material waste is reduced, the output quality is improved, and continuous data analysis helps to adjust chemical agent feeding and heating power; product consistency is guaranteed, automatic fault diagnosis is achieved, production parameters are adjusted in time, the service life of equipment is effectively prolonged, shutdown is reduced, and cost benefits are increased.

The invention relates to the technical field of equipment risk management and control, and particularly discloses an equipment risk management and control system based on model prediction control, which comprises a data acquisition processing module, a data analysis module, a model construction prediction module and an analysis regulation and control module, obtaining an equipment health condition score through standardization processing and weighted calculation; the data analysis module is used for distinguishing linear and nonlinear characteristic signals according to the score change trend; the model construction and prediction module selects corresponding algorithms to establish a prediction model according to different types of signals, so as to predict the health state of the equipment in the future; the analysis regulation and control module formulates a reasonable equipment maintenance plan based on the prediction result, and dynamically adjusts the production arrangement, the equipment state is continuously monitored and intelligently analyzed, faults can be effectively prevented, the probability of occurrence of non-planned shutdown events is reduced, and finally the purpose of improving production safety and economic benefits is achieved.

The invention discloses a lithium battery SOC (State of Charge) estimation method based on constrained optimization Kalman filtering. The method comprises the following steps: building a battery RC (Resistance-Capacitance) model; establishing a Kalman filtering model and initializing parameters; processing the collected battery data; iteratively updating Kalman filtering parameters continuously through constraint optimization to obtain more accurate values; mSE and RMSE are used for a Kalman filter to evaluate the performance of the filter. In order to overcome the limitation of the Kalman filter on parameter setting and the sensitivity of the Kalman filter on model change, the parameter adjustment process of the Kalman filter is optimized by introducing constraint conditions, and the parameters of the filter are dynamically adjusted in combination with an advanced optimization algorithm, so that the filter can keep the optimal estimation performance under different working conditions. Meanwhile, through limitation of constraint conditions, it is ensured that the estimation result conforms to the physical characteristics and actual working conditions of the battery, and the accuracy and reliability of SOC estimation are further improved.

The invention belongs to the technical field of sodium ion batteries, and particularly relates to a calcium ion doped modified sodium ion positive electrode material and a preparation method thereof, a positive electrode plate and a half battery. The calcium ion doped modified sodium ion positive electrode material comprises a sodium source, a doping source and a precursor, and is characterized in that the chemical general formula of the positive electrode material is Na < 1-x > M < x > Ni Fe Mn < c > O < 2 >, the value range of the doping sources M and x is x < = 0.05, a + b + c = 1, and all elements in the chemical formula meet charge balance. Sodium sites in a layered structure are replaced by calcium ion doping, so that interlayer spacing is enlarged, sodium ion transmission kinetics is enhanced, and adverse phase change is inhibited. The sodium ion positive electrode material shows high specific capacity and excellent cycle stability, the capacity retention ratio is 93.03% after 100 times of cycle charge and discharge at 1C, and the sodium ion positive electrode material has the characteristics of simple preparation, low cost, high specific capacity and excellent cycle performance.

The invention provides an industrial robot automatic control method and device and a computer readable storage medium, and the method comprises the steps: extracting the surface features of a to-be-polished workpiece through an image recognition technology, and controlling the initial force and torque of an end effector of an industrial robot based on the surface features of the to-be-polished workpiece; the surface temperature fluctuation of the workpiece in the to-be-polished process is monitored through a temperature sensor of the end effector of the industrial robot, and then the force and torque of the end effector of the industrial robot are adjusted; and power and state data in the polishing operation process are collected in real time, whether abnormal conditions exist or not is pre-judged through the deep learning technology, and the polishing operation is stopped immediately once the abnormal conditions are found. By means of the method and the corresponding device, the industrial robot can achieve intelligent automatic adjustment in the grinding and polishing process, and high performance and high reliability are kept under the complex working condition.

The invention provides a compressor air system management and control system based on an Internet of Things smart cloud platform. The compressor air system management and control system comprises an edge computing unit, the smart cloud platform and an intelligent terminal. And the edge calculation unit collects the operation parameters of the compressor in real time through the Internet of Things sensor, processes the collected data, generates preprocessed data, and executes a high-priority local quick response control task. And the smart cloud platform receives the data uploaded by the edge computing unit, performs fault prediction on the running state of the compressor by using a neural network model, dynamically calculates and optimizes running parameters, and sends an optimization result to the edge computing unit to execute adjustment. The intelligent terminal interacts with the intelligent cloud platform and the edge computing unit to display the real-time operation state and the fault prediction result of the compressor; in an emergency, the intelligent terminal cooperates with the edge computing unit through direct communication to execute localized emergency management. The operation efficiency, the fault response capability and the energy consumption optimization level of the compressor system are improved.

According to the power control method and system based on energy storage unit adjustment priority evaluation, an energy storage unit calibrates the charge state, the battery capacity and the health state of the energy storage unit and feeds back the charge state, the battery capacity and the health state to a battery management system; the battery management system verifies a built-in battery state estimation model of the battery management system by using the calibrated charge state and health state; based on the calibrated charge state and the battery capacity, calibrating the energy state range; calibrating the voltage standard deviation coefficient, the voltage range and the temperature range based on the measurement precision of the sensor; taking the weighted sum of each evaluation index score after calibration of the energy storage unit as a comprehensive evaluation score; determining the adjustment priority of the energy storage unit allowed to be adjusted by using the comprehensive evaluation score and the charge-discharge cycle times of the energy storage unit allowed to be adjusted; and selecting the adjustment-allowed energy storage unit with the highest adjustment priority as an execution unit for responding to the scheduling target power instruction, and ensuring the safe and stable operation of the energy storage system at the same time.

The invention discloses a UPS lithium battery system for an IDC data machine room, and relates to the technical field of battery monitoring, when the system operates, the problems of slow response to environmental influence and inaccurate evaluation in a traditional system are solved by dynamically acquiring a comprehensive influence index Timp of a microenvironment on battery temperature, and a strategy optimization module combines the operation requirement of a UPS lithium battery to improve the reliability of the system. According to the method, the optimization logic is triggered, and the optimized charging power Pr is dynamically calculated, so that the problems of unreasonable power distribution and insufficient battery protection in a battery charging strategy are effectively solved, the difference value of the actual charging power Po is monitored in real time, and the accuracy of an execution result and the safety of system operation are ensured. In addition, the evaluation feedback module monitors the running state of the UPS lithium battery after the dynamic adjustment charging strategy result is executed, and closed-loop optimization control is achieved. The method can adapt to the complex thermal environment of the IDC data room, reduce the overheating risk caused by the temperature rise of the battery, and significantly prolong the service life of the battery.

The invention discloses a method for predicting and suppressing harmonic waves generated by photovoltaic grid connection, which relates to the technical field of harmonic wave prediction, and comprises the following steps: detecting a three-phase current signal of a photovoltaic grid connection circuit by using an ip-iq detection method, and decomposing the three-phase current into an active component and a reactive component through Clarke conversion and Park conversion; according to the method, high-precision detection and separation of harmonic waves in the photovoltaic grid-connected system are realized by combining an ip-iq method with Clarke and Park transformation, dynamic modeling and harmonic signal prediction are performed by using the LSTM model, the defect of poor adaptability of a traditional method to nonlinear loads is overcome, and the prediction error is as low as 0.1%. Through combination of LSTM prediction and APF dynamic compensation, the compensation current opposite to the harmonic current is generated in real time, the harmonic suppression efficiency and the power grid stability are significantly improved, the electric energy quality is optimized, and the dynamic change of a complex load is adapted.

The invention relates to the technical field of air conditioners, in particular to an air conditioner internet-of-things control method. An IoT (Internet of Things) module is integrated in each air conditioner, and environment data including temperature, humidity and air quality of a room are collected in real time through connection of an intelligent sensor and a cloud platform; data of all air conditioning equipment is summarized to form a centralized management system of equipment state and environment information; an intelligent cooperative control algorithm based on communication between the equipment is constructed by means of internet-of-things intercommunication between the air conditioning equipment; the air conditioning equipment detects environmental data in real time through a local sensor, accesses prediction data of an external environment including weather forecast and climate change trend, and adjusts a working mode in advance in combination with a self-learning function of the equipment; a multi-dimensional energy-saving optimization algorithm is adopted for intelligent control; meanwhile, the overall energy consumption is optimized in combination with external weather change and equipment use frequency; the optimization strategy comprises time period distribution and equipment priority control; and performing personalized user control based on big data analysis.

The invention relates to a dynamic analysis optimization method for a high-speed stamping hybrid mechanism with a lubricating gap and considering an impact load. The dynamic analysis optimization method comprises the following steps: S1, defining structural parameters and design parameters; s2, establishing a lubrication gap motion model, a contact force model and an oil film bearing force model; s3, introducing a flexible beam unit model; s4, the relative motion state of the bearing and the shaft at the revolute pair element is judged; s5, establishing a kinetic equation; s6, solving the kinetic equation; s7, optimizing and improving the structural parameters of the mechanism; and S8, circulating the above process, and carrying out iterative solution until the kinetic analysis and parameter optimization are completed. The established rigid-flexible coupling kinetic equation containing the lubricating gap can more accurately predict the mechanism dynamic response characteristics of the high-speed stamping hybrid mechanism when the high-speed stamping hybrid mechanism is subjected to the impact load in actual work and operation, and the modeling method is suitable for dynamic response characteristic analysis and optimization design of high-speed stamping mechanisms of other configurations. The method has the advantages of wide applicability and the like.

The present invention relates to a catalyst for ortho-parahydrogen conversion suitable for a wide range of low-temperature conditions and a preparation method thereof. The catalyst is a fiber magnetic catalyst FeMnOx doped with Fe atoms in an orderly manner; the base material of the catalyst is α-MnO2, the α-MnO2 belongs to the tetragonal system, the unit cell parameter is #imgabs0#, the crystal structure is composed of 2×2 octahedral chains with shared edges, the XRD characteristic diffraction peaks are 2θ=12.8°, 18.1°, 25.7°, 37.5°, 49.8°, 56.3°, and the specific surface area measured by the BET test method is 100-150m 2 / g, and transmission electron microscopy shows that its microstructure is a fibrous structure with a diameter of 2 to 5nm and a length of 100nm to 500nm; when the catalyst is used for the ortho-parahydrogen conversion reaction, the ortho-parahydrogen conversion efficiency is more than 95%, and the catalyst life is more than 1400h; the wide range of low temperature conditions is 23K-77K. The coordination of Fe atoms with lattice oxygen in α-MnO2 can effectively avoid the deactivation of the catalyst caused by the change of the valence state of the metal component, thereby enhancing the stability and life of the catalyst, and is suitable for industrial-scale production.